17 citations · 47 across the 13 of their papers we have counts for
4 papers · 1 filter
Backdoor Attacks in Federated Learning by Rare Embeddings and Gradient Ensembling
KiYoon Yoo, Nojun Kwak
Recent advances in federated learning have demonstrated its promising capability to learn on decentralized datasets. However, a considerable amount of work has raised concerns due…
Dynamic Collective Intelligence Learning: Finding Efficient Sparse Model via Refined Gradients for Pruned Weights
Jangho Kim, Jayeon Yoo, Yeji Song +2
With the growth of deep neural networks (DNN), the number of DNN parameters has drastically increased. This makes DNN models hard to be deployed on resource-limited embedded system…
Edge Bias in Federated Learning and its Solution by Buffered Knowledge Distillation
Sangho Lee, Kiyoon Yoo, Nojun Kwak
Federated learning (FL), which utilizes communication between the server (core) and local devices (edges) to indirectly learn from more data, is an emerging field in deep learning…
On the Orthogonality of Knowledge Distillation with Other Techniques: From an Ensemble Perspective
SeongUk Park, KiYoon Yoo, Nojun Kwak
To put a state-of-the-art neural network to practical use, it is necessary to design a model that has a good trade-off between the resource consumption and performance on the test…